From Waste to Taste: How “Ugly” Labels Can Increase Purchase of Unattractive Produce
Bibliographic record
Abstract
Food producers and retailers throw away large amounts of perfectly edible produce that fails to meet appearance standards, contributing to the environmental issue of food waste. The authors examine why consumers discard aesthetically unattractive produce, and they test a low-cost, easy-to-implement solution: emphasizing the produce’s aesthetic flaw through “ugly” labeling (e.g., labeling cucumbers with cosmetic defects “Ugly Cucumbers” on store displays or advertising). Seven experiments, including two conducted in the field, demonstrate that “ugly” labeling corrects for consumers’ biased expectations regarding key attributes of unattractive produce—particularly tastiness—and thus increases purchase likelihood. “Ugly” labeling is most effective when associated with moderate (rather than steep) price discounts. Against managers’ intuition, it is also more effective than alternative labeling that does not exclusively point out the aesthetic flaw, such as “imperfect” labeling. This research provides clear managerial recommendations on the labeling and the pricing of unattractive produce while addressing the issue of food waste.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".